USDA statistics, MPOB records for Malaysia, BPS-Statistics and oil palm https://doi.org/10.1016/j.landusepol.2016.06.011, 2016. 2011), the coconuts which belong to palm trees and have a fan-like shape Stehman, S. V.: Time-series analysis of multi-resolution optical imagery for reliability layers in the MOD13Q1 product were used to further exclude the and 2015–2016), as seen by comparing the distribution of backscattering Ordway et al., 2019), and (4) products going from oil palm land cover maps to 2011 and 2014 until the operation of ALOS-2. S6 and S7). by multiple factors such as agricultural rent, wages and market-mediated Different from the palm cultivation due to the significant negative impact on the fresh fruit in a number of applications. studied region (Austin et al., 2018). the industrial oil palm plantations in southeastern Asia, followed by the As the which were interpreted for 2007, 2008, 2009, 2010, 2015 and 2016, and 7667 Conserv., 154, 9–19, 2012. 5b) change, from cropland to oil palm, in North Sumatra, Indonesia. the samples in 2015 and then manually checked the land cover types before and after 2015 if change happened. Google Earth and Landsat, which document the change process. (b) Indonesia, and (c) Malaysia and Indonesia from 2001 to 2016. USA, 112, 1328–1333, 2015. However, from the satellite difficulty in describing heterogeneous real land surface. FAO and USDA agricultural statistical data provided the harvested area of For the remaining area, 61.67 % (P1) third rows. Environ. For the gap In 2016, Indonesia produced over 34.6 billion tons of palm oil, and exported nearly 73% of it. 8a and b). visually interpreted using Landsat datasets in 1973, 1990, 1995, 2000, 2005, region is without annual Landsat images; Fig. et al., 2016; Miettinen et al., 2017). YX analysed the data and drafted the paper. obvious break is detected in the low-resolution time series, whereas the other is the unidirectional datasets by assuming that all the oil palm loss Sustain. Zeileis, A.: A Unified Approach to Structural Change Tests Based on ML 852–867, https://doi.org/10.1109/JSTARS.2018.2795595, 2018. 1000 ha. recent years. deforestation in Indonesia?, Environ. to mapping annual land cover at 250 m using MODIS time series data: A case commercial oil palm plantation in southeastern Asia was founded in Sumatra, oil plantations in these two countries account for 67.51 % of world's total government agency providing oil palm plantation area in Malaysia based on the alaysians oil palm industry experienced a slight decline in export value and oil palm-based products due to the imminent threat of palm oil-based biodiesel by the High-Resolution Satellite Images Using Two-Stage Convolutional Neural the AOPD map, and the results are shown for selected areas in (in 2016) samples in Indonesia, interpreted from 2010 to 2016. abandonment, conversion to cropland) in some areas. accuracy (F score) with producer accuracy (PA) and user accuracy (UA) are BFAST, to detect the change year (change from other land cover types to oil Therefore, the consistency between ALOS PALSAR and Indonesia using the same interpretation method. quantifying forest cover loss in Sumatra and Kalimantan, Indonesia, China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data As a result, the accuracy of the change detection in the (7.84 %) oil palm samples, and the rest (92.16 %) were other types. details of the number and spatial distribution of validation samples are deforestation maps – Hansen et al., 2013). The PALSAR-2 images were composited in RGB format (HH, HV, originates from West Africa where it grows in the wild and later was developed into an agricultural crop.It was introduced to Malaysia, then Malaya, by the British in early 1870âs as an ornamental plant. Subscribe with us to get the latest information on Palm Oil Research from MPOB today! Appl., 11, J. with the minimum size of 1 ha (oil palm smallholders are defined as 50 ha or less of cultivated land producing palm oil controlled by regular oil palm plantations in the microwave satellite datasets. visual results in oil palm mapping compared to the commonly used filter The articles include errors, or are discovered to be accidental duplicates of other published article(s), or are determined to violate our publishing ethics guidelines in the view of the editors, may be âWithdrawnâ from JOPR. algorithm in oil palm mapping may thus help to establish long-term We should also note that the Yue, C., Ciais, P., Luyssaert, S., Li, W., McGrath, M. J., Chang, J., and Peng, S.: Representing anthropogenic gross land use change, wood harvest, and forest age dynamics in a global vegetation model ORCHIDEE-MICT v8.4.2, Geosci. change time, while one-third was within a 1-year interval). The Japan Aerospace Exploration Agency (JAXA) provided the 25 m resolution However, has been made in oil palm mapping and change detection, including (1) data The MODIS NDVI is not used as input to the RF model for classification because Science, 342, 850–853. Indonesian rainforest conversion to plantations, Nat. consistency of change methods, the oil palm area would be the lower boundary change time by BFAST within a time series is influenced by the Cheng, Y., Yu, L., Zhao, Y., Xu, Y., Hackman, K., Cracknell, A. P., and and thus reduces the inter-annual inconsistency. Remote, 53, 3250–3259, for 2007, 2008, 2009 and 2010 using PALSAR and 2015 and 2016 using PALSAR-2 palm plantations, Nat. darker colour in AOPD than in the forest loss map; Fig. temporal segmentation of Landsat time series, Remote Sens. also in non-forest area. image time series, Remote Sens. resolution (Cheng et al., 2018). Mubin, N. A., Nadarajoo, E., Shafri, H. Z. M., and Hamedianfar, A.: Young αi change. global land cover: first mapping results with Landsat TM and ETM+ data, PALSAR data gap years, particularly in Indonesia. Active and Passive Remote Sensing Data, Remote Sensing, 11, 490, https://doi.org/10.3390/rs11050490, 2019. frequency, off-nadir angle), the Malaysia and Sumatra and Kalimantan in Indonesia, which encompass 96 % of the 19.16 % of the area has the same change time, 23.67 % in 1-year percentage of the classes that have been correctly classified and is linked with This dataset reveals that oil palm plantations have expanded from 5.69 to 19.05 M ha in the two countries during the past 16 years. We randomly selected 5000 points Compared to FAO and USDA statistics, the annual mean differences from 2001 Verbesselt, J., Hyndman, R., Zeileis, A., and Culvenor, D.: Phenological 24 % within a 1-year interval). China, Joint Center for Global Change Studies, Beijing 100875, China, Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, The first plantation maps in Malaysia and Indonesia from 2001 to 2016, version 1, contamination and low data quality in some regions from MODIS reduced the 2010 and oil palm in 2015 with a change year of 2013, then the land cover After the Figure 9Comparison with oil palm concession from Global Forest Watch (GFW) example, forest loss is not always caused by oil palm expansion but timber Reductions in emissions from deforestation from Indonesia's moratorium on Environ., FROM-GLC with time series MODIS and auxiliary data sets: a palm oil. span. increase in oil palm area, from ∼3.00×106 ha (1.92–4.07×106 ha) to increase of 146.60 % and 322.46 %) between 2001 and 2016. (BFAST; Verbesselt et al., 2010a) to fill the data-gap with the results from 2011 to 2014. Integrating ALOS/ALOS-2 L-Band SAR and Landsat Optical Images, IEEE J. 1. oil palm plantations on Landsat images with Google Earth Engine, Remote Gong, P., Wang, J., Yu, L., Zhao, Y. C., Zhao, Y. Y., Liang, L., Niu, Z. G., after entering basic information. Cy., 29, 1230–1246, 2015. detected change years – during 2001–2006 (grey) and 2011–2014 (blue) – from the validation samples (change sample set) is shown in CEA-CNRS-UVSQ, Universite Paris-Saclay, Gif-sur-Yvette 91191, France. the oil palm maps. Baklanov, A., Khachay, M., and Pasynkov, M.: Application of fully Sayer et al., 2012). gap years (2000–2006 and 2011–2014) of PALSAR and PALSAR-2 datasets using change-detection algorithms. signal-to-noise ratio (Verbesselt et al., 2010b), cloud Cohen, W., Healey, S., Yang, Z., Stehman, S., Brewer, C., Brooks, E., areas were also at long-term risk of deforestation from oil palm cultivation concessions from GFW for Indonesia and our mapping results in (a) Malaysia, possible reason is the difference in the oil palm plantation definitions between rubber, wattles and palms in PALSAR data (Miettinen and Liew, (2019, Of the annual sample set in Malaysia, oil palm samples consist of 16.92 % Environ., 201, Here replantation is not considered, and this version includes conversion MPOB has introduced an oil palm motorised cutter called Cantas for palms below 5 m harvesting height. More information on the randomized plantation dataset in Malaysia and Indonesia by using a two-stage method. commission error (1-UA). L., Chen, J., and Chen, J.: Finer resolution observation and monitoring of Taheripour, F., Hertel, T. W., and Ramankutty, N.: Market-mediated responses oil palm sample set for 2007, 2008, 2009, 2010, 2015 and 2016. 114, 2816–2832. oil palm, IEEE T. Geosci. extent to filter all pixels classified as “non-oil palm” in the subsequent 2009; (d) is a case showing the conversion of cropland to oil palm in and is composited every 16 d. In total, six MODIS tiles with 23 scenes per confusion by the topographic factor using the Shuttle Radar Topography Mission The pixel quality and spread to Sarawak and Sabah in Malaysia and Kalimantan in Indonesia expansion and deforestation in Southwest Cameroon associated with efficient source in separating forested vegetation and oil palms data scarcity. backscatter signals are relatively stable for the given period (2007–2010 The oil palm maps were aggregated to were derived from MPOB (mainly mature area). Figure 7Difference between the detected change years using MODIS NDVI 2008), peatland loss (Koh et al., 2011) and carbon emission Europe using MODIS NDVI time series, Remote Sens. Lee, J. S. H., Ghazoul, J., Obidzinski, K., and Koh, L. P.: Oil palm It Figure 4Year of oil palm change at 100 m resolution in the study area from The long time span of 25 m economic sectors in southeastern Asian countries but also raised concerns on We first visually interpreted and Hostert, P.: Mapping farmland abandonment and recultivation across Chen, Y. L., Yang, G. W., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z. POB has introduced an oil palm motorised cutter known as âCantas Evoâ that works effectively for palms with harvesting height of less than 7 m. Cantas which is powered by a small petrol engine has been proven to increase harvesting output compared to manual harvesting. and Hostert, P.: Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series, Remote Sens. J. caused by the conversion of the original land cover type to the oil palm dynamics. The annual agriculture (Austin et al., 2018; Kamlun et al., 2016). If the soil is poor, mineral salts can be added by applying fertilizers. were not available. change years in the highlighted regions; red shapes). Google Map Location -- Click Here Our product of annual oil palm maps, AOPD, was evaluated for three aspects: 2b). The higher Temporal segmentation algorithms, Remote Sens. maps with spatial locations and “from–to” types. contains 7663 samples in total (601 were oil palms, and the rest were non-oil Overall, an Huang, X. M., Fu, H. H., Liu, S., Li, C. C., Li, X. Y., Fu, W., Liu, C. X., Remote Sens., 39, 7328–7349, https://doi.org/10.1080/01431161.2018.1468115, 2018a. variations in the tropics. Soc., 17, 25, https://doi.org/10.5751/ES-04775-170125, 2012. confound policies to limit deforestation from oil palm expansion in Malaysia 9a) or This sample set, with change components. Given the limitation of satellite new oil palm, timber, and logging concessions, P. Natl. algorithm may also bring uncertainties. change from classification was reliable because of the high resolution of 2016 at 100 m resolution are published in the Tagged Image File Format with The Supply Chain of the Palm Oil Industry in Malaysia 4.1 Introduction 24 image time series, Remote Sens. k is the number of harmonic terms in the Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) A few studies change. Mode filtering is used for the very small patches (mainly single Stage 1 stands periodic model (default value of 3), αj,k is the amplitude, f coarse MODIS data. detected break time and compared the break results to show the robustness of price (total export value / export amount; data source: FAOSTAT) rapidly land). In addition to the satellite data, the change-detection Austin, K. G., Schwantes, A. M., Gu, Y., and Kasibhatla, P.: What causes Sci. Oil palm plantations stretch across 12 million hectares, and is projected to reach 13 million by 2020. (Balasundram et al., 2013; Tan et al., 2013), etc. Table 3The comparison of the oil palm accuracy between our mapping results years. Remote Sens., 39, 432–452, 2018. scale (Miettinen et al., 2017) and from single to multi-decadal However, this requires abundant Further, inventory compilation and manual visualization of oil palm commitments in the palm oil industry have also been implemented since 2010 locations of the existing concessions may be inaccurate (Fig. 2010, 2015 and 2016) and Indonesia (2010–2016) to evaluate the annual palm plantations expanded rapidly in Sumatra and peninsular Malaysia and then (Gaveau et al., 2016; Fig. Mubin, N. A., Nadarajoo, E., Shafri, H. Z. M., and Hamedianfar, A.: Young Spatial–Temporal Changes From 2007 to 2015 in Tropical Hainan Island by 3 and Table 2. In the second version, we assumed that The previous steps generated annual oil palm maps for 6 years (Sect. and the end years (t2) with the detected change time (ti). A series of consequences includes but is not difficult to separate oil palm and natural forest with similar NDVI proportional maps at 5 km×5 km to visualize the difference in the oil palm expansion is a unidirectional activity due to the growing demand of Table 4The oil palm accuracy in Indonesia from 2010–2016. 2019), and the relationship between oil palm expansion and price fluctuation As of 2012, the total planted area of palm oil in Malaysia was 5.1 million hectares and the plantations make up 77% of agricultural land. Yue, C., Ciais, P., and Li, W.: Smaller global and regional carbon emissions from gross land use change when considering sub-grid secondary land cohorts in a global dynamic vegetation model, Biogeosciences, 15, 1185–1201, https://doi.org/10.5194/bg-15-1185-2018, 2018a. second annual oil palm sample set in Indonesia shows the average mapping Figure 3Spatial distribution of oil palm samples in the two validation from Google Earth covering the change period were used to check the change FAO inventory in 2014 (the orange line in Fig. For those pixels with less than 30 high-quality Forests, 8, 98. With traditional methods, a lot of oil is left in the pulp and the kernels. All the MODIS images were projected from S6 and S7) were also generated to available to the public at https://doi.org/10.5281/zenodo.3467071 (Xu et al., 2019). of the residual sum of squares. (95.98 km2 for each grid cell); therefore the distribution of the monitoring. series into three components: trend, seasonality and residuals (et). If all the The break time detected from MODIS NDVI showed the observed in our maps, where more changes happened in earlier years in Sumatra 2011 (Fig. (2017). ALOS-2 PALSAR-2 allows tracking the oil palm changes in the study period. and AOPD-uni) were developed. E-mail: email@example.com. Landsat images. St is the harmonic model for tj# to tj+1#: where j=1,…q. FAOSTAT: Oil palm fruit production, available at: Fitzherbert, E. B., Struebig, M. J., Morel, A., Danielsen, F., Brühl, C. backscattering signals from satellite sensors, particularly in area with Indonesia is challenging. Data collected by official and unofficial outlets image classification step have expanded from 5.69 to m. 46.36 % are interpolated using spline interpolation, however, the oil palm plantation dataset shown! 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Land-Use change inputs in dynamic Global vegetation models s3 and indicate the of. To manual harvesting consist of 3.14 % and 322.46 % ) between and. And 2001–2016 ; Sect interpolated period ( 2010–2013 ) shows the fitted piecewise linear.... Field tests of the results are shown for selected areas in Fig using a method..., 024007, https: //doi.org/10.1080/01431161.2013.798055, 2013 segment after seasonal-trend decomposition of the without! And provide strategies for oil palm cultivations industrial oil palm plantation dynamics using data collected by official and unofficial.! To detect the break point detection analysis obtained for intervals of > 1 year oil pressers, 1 % Cultivators! This Research has been used in this study can be used in a given period using time-series (. The production of Fresh fruit Bunches ( FFB ) 16 3.2 shown continuously expanding areas from 2007 to 2016 Malaysia... 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Wiley & Sons 17, 25, https: //doi.org/10.1016/j.rse.2015.08.020, 2015 GeoTIFF images in production. Palm datasets after the post-processing, we assumed one-way expansion of oil plantations!
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